
"""simple docstring"""



import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import CvtForImageClassification, CvtModel
 from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




class 		a_    (			_UpperCAmelCase						):







    def   _snake_case       (      self      :							List[str]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.config_class(**self.inputs_dict  )
        self.parent.assertTrue(hasattr(__UpperCamelCase  ,       """embed_dim"""  )  )
        self.parent.assertTrue(hasattr(__UpperCamelCase  ,       """num_heads"""  )  )







class 		a_    :







    def __init__(      self      :							Union[str, Any]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Any=13  ,       __UpperCamelCase      :							Dict=64  ,       __UpperCamelCase      :							str=3  ,       __UpperCamelCase      :							str=[16, 48, 96]  ,       __UpperCamelCase      :							Optional[Any]=[1, 3, 6]  ,       __UpperCamelCase      :							str=[1, 2, 10]  ,       __UpperCamelCase      :							List[str]=[7, 3, 3]  ,       __UpperCamelCase      :							Tuple=[4, 2, 2]  ,       __UpperCamelCase      :							Optional[int]=[2, 1, 1]  ,       __UpperCamelCase      :							List[Any]=[2, 2, 2]  ,       __UpperCamelCase      :							str=[False, False, True]  ,       __UpperCamelCase      :							str=[0.0, 0.0, 0.0]  ,       __UpperCamelCase      :							Any=0.0_2  ,       __UpperCamelCase      :							List[str]=1e-12  ,       __UpperCamelCase      :							Tuple=True  ,       __UpperCamelCase      :							List[str]=True  ,       __UpperCamelCase      :							Optional[Any]=2  ,       )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = parent
        _UpperCAmelCase        = batch_size
        _UpperCAmelCase        = image_size
        _UpperCAmelCase        = patch_sizes
        _UpperCAmelCase        = patch_stride
        _UpperCAmelCase        = patch_padding
        _UpperCAmelCase        = is_training
        _UpperCAmelCase        = use_labels
        _UpperCAmelCase        = num_labels
        _UpperCAmelCase        = num_channels
        _UpperCAmelCase        = embed_dim
        _UpperCAmelCase        = num_heads
        _UpperCAmelCase        = stride_kv
        _UpperCAmelCase        = depth
        _UpperCAmelCase        = cls_token
        _UpperCAmelCase        = attention_drop_rate
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = layer_norm_eps







    def   _snake_case       (      self      :							str  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        _UpperCAmelCase        = None
        if self.use_labels:
            _UpperCAmelCase        = ids_tensor([self.batch_size]  ,       self.num_labels  )

        _UpperCAmelCase        = self.get_config()
        return config, pixel_values, labels







    def   _snake_case       (      self      :							Optional[Any]  )		->Any:






        '''simple docstring'''






        return CvtConfig(
            image_size=self.image_size  ,       num_labels=self.num_labels  ,       num_channels=self.num_channels  ,       embed_dim=self.embed_dim  ,       num_heads=self.num_heads  ,       patch_sizes=self.patch_sizes  ,       patch_padding=self.patch_padding  ,       patch_stride=self.patch_stride  ,       stride_kv=self.stride_kv  ,       depth=self.depth  ,       cls_token=self.cls_token  ,       attention_drop_rate=self.attention_drop_rate  ,       initializer_range=self.initializer_range  ,       )







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Any  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = CvtModel(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  )
        _UpperCAmelCase        = (self.image_size, self.image_size)
        _UpperCAmelCase     ,_UpperCAmelCase        = image_size[0], image_size[1]
        for i in range(len(self.depth  )  ):
            _UpperCAmelCase        = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1  )
            _UpperCAmelCase        = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,       (self.batch_size, self.embed_dim[-1], height, width)  )







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							List[Any]  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = self.num_labels
        _UpperCAmelCase        = CvtForImageClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.parent.assertEqual(result.logits.shape  ,       (self.batch_size, self.num_labels)  )







    def   _snake_case       (      self      :							Dict  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = self.prepare_config_and_inputs()
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = config_and_inputs
        _UpperCAmelCase        = {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					Optional[Any]			     =					(CvtModel, CvtForImageClassification) if is_torch_available() else ()
    a   :					Tuple			     =					(
        {'feature-extraction': CvtModel, 'image-classification': CvtForImageClassification}
        if is_torch_available()
        else {}
    )

    a   :					Tuple			     =					False
    a   :					List[Any]			     =					False
    a   :					Dict			     =					False
    a   :					Any			     =					False
    a   :					Union[str, Any]			     =					False







    def   _snake_case       (      self      :							str  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = CvtModelTester(self  )
        _UpperCAmelCase        = ConfigTester(self  ,       config_class=__UpperCamelCase  ,       has_text_modality=__UpperCamelCase  ,       hidden_size=37  )







    def   _snake_case       (      self      :							List[Any]  )		->Dict:






        '''simple docstring'''






        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()







    def   _snake_case       (      self      :							Union[str, Any]  )		->Union[str, Any]:






        '''simple docstring'''






        return







    @unittest.skip(reason="""Cvt does not output attentions"""  )
    def   _snake_case       (      self      :							Dict  )		->Any:






        '''simple docstring'''






        pass







    @unittest.skip(reason="""Cvt does not use inputs_embeds"""  )
    def   _snake_case       (      self      :							List[str]  )		->Tuple:






        '''simple docstring'''






        pass







    @unittest.skip(reason="""Cvt does not support input and output embeddings"""  )
    def   _snake_case       (      self      :							str  )		->List[str]:






        '''simple docstring'''






        pass







    def   _snake_case       (      self      :							Optional[int]  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            _UpperCAmelCase        = model_class(__UpperCamelCase  )
            _UpperCAmelCase        = inspect.signature(model.forward  )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            _UpperCAmelCase        = [*signature.parameters.keys()]

            _UpperCAmelCase        = ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]  ,       __UpperCamelCase  )







    def   _snake_case       (      self      :							List[Any]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase  )







    def   _snake_case       (      self      :							str  )		->Any:






        '''simple docstring'''






        def check_hidden_states_output(__UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							str  ):
            _UpperCAmelCase        = model_class(__UpperCamelCase  )
            model.to(__UpperCamelCase  )
            model.eval()

            with torch.no_grad():
                _UpperCAmelCase        = model(**self._prepare_for_class(__UpperCamelCase  ,       __UpperCamelCase  )  )

            _UpperCAmelCase        = outputs.hidden_states

            _UpperCAmelCase        = len(self.model_tester.depth  )
            self.assertEqual(len(__UpperCamelCase  )  ,       __UpperCamelCase  )

            # verify the first hidden states (first block)
            self.assertListEqual(
                list(hidden_states[0].shape[-3:]  )  ,       [
                    self.model_tester.embed_dim[0],
                    self.model_tester.image_size // 4,
                    self.model_tester.image_size // 4,
                ]  ,       )

        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            _UpperCAmelCase        = True
            check_hidden_states_output(__UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            _UpperCAmelCase        = True

            check_hidden_states_output(__UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase  )







    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
    def   _snake_case       (      self      :							str  )		->List[Any]:






        '''simple docstring'''






        pass







    @slow
    def   _snake_case       (      self      :							str  )		->int:






        '''simple docstring'''






        for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _UpperCAmelCase        = CvtModel.from_pretrained(__UpperCamelCase  )
            self.assertIsNotNone(__UpperCamelCase  )







def 			_UpperCamelCase						(    )     ->	List[str]:





    """simple docstring"""

    _UpperCAmelCase        = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
    return image







@require_torch
@require_vision
class 		a_    (			unittest.TestCase						):







    @cached_property
    def   _snake_case       (      self      :							List[str]  )		->int:






        '''simple docstring'''






        return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )







    @slow
    def   _snake_case       (      self      :							List[Any]  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(__UpperCamelCase  )

        _UpperCAmelCase        = self.default_image_processor
        _UpperCAmelCase        = prepare_img()
        _UpperCAmelCase        = image_processor(images=__UpperCamelCase  ,       return_tensors="""pt"""  ).to(__UpperCamelCase  )

        # forward pass
        with torch.no_grad():
            _UpperCAmelCase        = model(**__UpperCamelCase  )

        # verify the logits
        _UpperCAmelCase        = torch.Size((1, 10_00)  )
        self.assertEqual(outputs.logits.shape  ,       __UpperCamelCase  )

        _UpperCAmelCase        = torch.tensor([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0]  ).to(__UpperCamelCase  )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,       __UpperCamelCase  ,       atol=1e-4  )  )

"""simple docstring"""



import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer




class 		a_    :







    def __init__(      self      :							Any  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							Dict=13  ,       __UpperCamelCase      :							Tuple=7  ,       __UpperCamelCase      :							List[Any]=True  ,       __UpperCamelCase      :							List[str]=True  ,       __UpperCamelCase      :							Union[str, Any]=False  ,       __UpperCamelCase      :							List[str]=True  ,       __UpperCamelCase      :							str=99  ,       __UpperCamelCase      :							Optional[Any]=32  ,       __UpperCamelCase      :							Union[str, Any]=5  ,       __UpperCamelCase      :							Tuple=4  ,       __UpperCamelCase      :							Any=37  ,       __UpperCamelCase      :							str="gelu"  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							int=0.1  ,       __UpperCamelCase      :							List[str]=5_12  ,       __UpperCamelCase      :							Union[str, Any]=16  ,       __UpperCamelCase      :							Union[str, Any]=2  ,       __UpperCamelCase      :							Union[str, Any]=0.0_2  ,       __UpperCamelCase      :							List[str]=3  ,       __UpperCamelCase      :							Optional[Any]=4  ,       __UpperCamelCase      :							str=None  ,       )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = parent
        _UpperCAmelCase        = batch_size
        _UpperCAmelCase        = seq_length
        _UpperCAmelCase        = is_training
        _UpperCAmelCase        = use_input_mask
        _UpperCAmelCase        = use_token_type_ids
        _UpperCAmelCase        = use_labels
        _UpperCAmelCase        = vocab_size
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = max_position_embeddings
        _UpperCAmelCase        = type_vocab_size
        _UpperCAmelCase        = type_sequence_label_size
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = num_labels
        _UpperCAmelCase        = num_choices
        _UpperCAmelCase        = scope







    def   _snake_case       (      self      :							Optional[int]  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = ids_tensor([self.batch_size, self.seq_length]  ,       self.vocab_size  )

        _UpperCAmelCase        = None
        if self.use_input_mask:
            _UpperCAmelCase        = random_attention_mask([self.batch_size, self.seq_length]  )

        _UpperCAmelCase        = None
        if self.use_token_type_ids:
            _UpperCAmelCase        = ids_tensor([self.batch_size, self.seq_length]  ,       self.type_vocab_size  )

        _UpperCAmelCase        = None
        _UpperCAmelCase        = None
        _UpperCAmelCase        = None
        if self.use_labels:
            _UpperCAmelCase        = ids_tensor([self.batch_size]  ,       self.type_sequence_label_size  )
            _UpperCAmelCase        = ids_tensor([self.batch_size, self.seq_length]  ,       self.num_labels  )
            _UpperCAmelCase        = ids_tensor([self.batch_size]  ,       self.num_choices  )

        _UpperCAmelCase        = self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







    def   _snake_case       (      self      :							str  )		->Any:






        '''simple docstring'''






        return LlamaConfig(
            vocab_size=self.vocab_size  ,       hidden_size=self.hidden_size  ,       num_hidden_layers=self.num_hidden_layers  ,       num_attention_heads=self.num_attention_heads  ,       intermediate_size=self.intermediate_size  ,       hidden_act=self.hidden_act  ,       hidden_dropout_prob=self.hidden_dropout_prob  ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,       max_position_embeddings=self.max_position_embeddings  ,       type_vocab_size=self.type_vocab_size  ,       is_decoder=__UpperCamelCase  ,       initializer_range=self.initializer_range  ,       )







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							Optional[Any]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = LlamaModel(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  )
        _UpperCAmelCase        = model(__UpperCamelCase  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,       (self.batch_size, self.seq_length, self.hidden_size)  )







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Union[str, Any]  ,       )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = True
        _UpperCAmelCase        = LlamaModel(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       )
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       )
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,       (self.batch_size, self.seq_length, self.hidden_size)  )







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							int  ,       )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = LlamaForCausalLM(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.parent.assertEqual(result.logits.shape  ,       (self.batch_size, self.seq_length, self.vocab_size)  )







    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Dict  ,       )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = True
        _UpperCAmelCase        = True
        _UpperCAmelCase        = LlamaForCausalLM(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()

        # first forward pass
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       use_cache=__UpperCamelCase  ,       )
        _UpperCAmelCase        = outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        _UpperCAmelCase        = ids_tensor((self.batch_size, 3)  ,       config.vocab_size  )
        _UpperCAmelCase        = ids_tensor((self.batch_size, 3)  ,       vocab_size=2  )

        # append to next input_ids and
        _UpperCAmelCase        = torch.cat([input_ids, next_tokens]  ,       dim=-1  )
        _UpperCAmelCase        = torch.cat([input_mask, next_mask]  ,       dim=-1  )

        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       output_hidden_states=__UpperCamelCase  ,       )["""hidden_states"""][0]
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       past_key_values=__UpperCamelCase  ,       output_hidden_states=__UpperCamelCase  ,       )["""hidden_states"""][0]

        # select random slice
        _UpperCAmelCase        = ids_tensor((1,)  ,       output_from_past.shape[-1]  ).item()
        _UpperCAmelCase        = output_from_no_past[:, -3:, random_slice_idx].detach()
        _UpperCAmelCase        = output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-3  )  )







    def   _snake_case       (      self      :							Tuple  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = self.prepare_config_and_inputs()
        (
            (
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,
        )        = config_and_inputs
        _UpperCAmelCase        = {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict







@require_torch
class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					Optional[Any]			     =					(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
    a   :					Any			     =					(LlamaForCausalLM,) if is_torch_available() else ()
    a   :					Union[str, Any]			     =					(
        {
            'feature-extraction': LlamaModel,
            'text-classification': LlamaForSequenceClassification,
            'text-generation': LlamaForCausalLM,
            'zero-shot': LlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    a   :					str			     =					False
    a   :					List[str]			     =					False







    def   _snake_case       (      self      :							Union[str, Any]  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = LlamaModelTester(self  )
        _UpperCAmelCase        = ConfigTester(self  ,       config_class=__UpperCamelCase  ,       hidden_size=37  )







    def   _snake_case       (      self      :							Optional[int]  )		->Optional[Any]:






        '''simple docstring'''






        self.config_tester.run_common_tests()







    def   _snake_case       (      self      :							Dict  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase  )







    def   _snake_case       (      self      :							int  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            _UpperCAmelCase        = type
            self.model_tester.create_and_check_model(*__UpperCamelCase  )







    def   _snake_case       (      self      :							Tuple  )		->int:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = input_dict["""input_ids"""]
        _UpperCAmelCase        = input_ids.ne(1  ).to(__UpperCamelCase  )
        _UpperCAmelCase        = ids_tensor([self.model_tester.batch_size]  ,       self.model_tester.type_sequence_label_size  )
        _UpperCAmelCase        = LlamaForSequenceClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.assertEqual(result.logits.shape  ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )







    def   _snake_case       (      self      :							Dict  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = """single_label_classification"""
        _UpperCAmelCase        = input_dict["""input_ids"""]
        _UpperCAmelCase        = input_ids.ne(1  ).to(__UpperCamelCase  )
        _UpperCAmelCase        = ids_tensor([self.model_tester.batch_size]  ,       self.model_tester.type_sequence_label_size  )
        _UpperCAmelCase        = LlamaForSequenceClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.assertEqual(result.logits.shape  ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )







    def   _snake_case       (      self      :							List[str]  )		->str:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = """multi_label_classification"""
        _UpperCAmelCase        = input_dict["""input_ids"""]
        _UpperCAmelCase        = input_ids.ne(1  ).to(__UpperCamelCase  )
        _UpperCAmelCase        = ids_tensor(
            [self.model_tester.batch_size, config.num_labels]  ,       self.model_tester.type_sequence_label_size  ).to(torch.float  )
        _UpperCAmelCase        = LlamaForSequenceClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.assertEqual(result.logits.shape  ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )







    @unittest.skip("""LLaMA buffers include complex numbers, which breaks this test"""  )
    def   _snake_case       (      self      :							Dict  )		->int:






        '''simple docstring'''






        pass







    @parameterized.expand([("""linear""",), ("""dynamic""",)]  )
    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							Tuple  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = ids_tensor([1, 10]  ,       config.vocab_size  )
        _UpperCAmelCase        = ids_tensor([1, int(config.max_position_embeddings * 1.5  )]  ,       config.vocab_size  )

        set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
        _UpperCAmelCase        = LlamaModel(__UpperCamelCase  )
        original_model.to(__UpperCamelCase  )
        original_model.eval()
        _UpperCAmelCase        = original_model(__UpperCamelCase  ).last_hidden_state
        _UpperCAmelCase        = original_model(__UpperCamelCase  ).last_hidden_state

        set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
        _UpperCAmelCase        = {"""type""": scaling_type, """factor""": 1_0.0}
        _UpperCAmelCase        = LlamaModel(__UpperCamelCase  )
        scaled_model.to(__UpperCamelCase  )
        scaled_model.eval()
        _UpperCAmelCase        = scaled_model(__UpperCamelCase  ).last_hidden_state
        _UpperCAmelCase        = scaled_model(__UpperCamelCase  ).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
            self.assertTrue(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-5  )  )
        else:
            self.assertFalse(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-5  )  )

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-5  )  )







@require_torch
class 		a_    (			unittest.TestCase						):







    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
    @slow
    def   _snake_case       (      self      :							str  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor([input_ids]  )  )
        # Expected mean on dim = -1
        _UpperCAmelCase        = torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]  )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,       __UpperCamelCase  ,       atol=1e-5  ,       rtol=1e-5  )







    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
    @slow
    def   _snake_case       (      self      :							Optional[int]  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor(__UpperCamelCase  )  )
        # Expected mean on dim = -1
        _UpperCAmelCase        = torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]  )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,       __UpperCamelCase  ,       atol=1e-5  ,       rtol=1e-5  )







    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
    @slow
    def   _snake_case       (      self      :							Dict  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor(__UpperCamelCase  )  )
        # Expected mean on dim = -1
        _UpperCAmelCase        = torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]  )
        # fmt: on
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )







    @unittest.skip(
        """Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"""  )
    @slow
    def   _snake_case       (      self      :							str  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor(__UpperCamelCase  )  )

        _UpperCAmelCase        = torch.tensor(
            [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]  ,       dtype=torch.floataa  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]  )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,       __UpperCamelCase  ,       atol=1e-5  ,       rtol=1e-5  )







    @unittest.skip("""Model is curently gated"""  )
    @slow
    def   _snake_case       (      self      :							Tuple  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
        _UpperCAmelCase        = """Simply put, the theory of relativity states that """
        _UpperCAmelCase        = LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""  )
        _UpperCAmelCase        = tokenizer.encode(__UpperCamelCase  ,       return_tensors="""pt"""  )
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained(
            """meta-llama/Llama-2-13b-chat-hf"""  ,       device_map="""sequential"""  ,       use_safetensors=__UpperCamelCase  )

        # greedy generation outputs
        _UpperCAmelCase        = model.generate(__UpperCamelCase  ,       max_new_tokens=64  ,       top_p=__UpperCamelCase  ,       temperature=1  ,       do_sample=__UpperCamelCase  )
        _UpperCAmelCase        = tokenizer.decode(generated_ids[0]  ,       skip_special_tokens=__UpperCamelCase  )
        self.assertEqual(__UpperCamelCase  ,       __UpperCamelCase  )